Los Angeles Crime Dashboard

Summary

This page analyzes crime patterns in Los Angeles by looking at trends over time, the most common crime types, crime hotspots by ZIP code, and victim age characteristics. These visualizations help identify when crimes peak, which offenses are most frequent, and how different groups of victims are affected.

LA Crime Map (Basic Marker Map)

import folium
from folium.plugins import MarkerCluster
import pandas as pd

df = pd.read_csv("data/los_angeles.csv")

la_map = folium.Map(location=[34.0522, -118.2437], zoom_start=10)
marker_cluster = MarkerCluster().add_to(la_map)

df_sample = df.sample(n=5000, random_state=42)

for _, row in df_sample.iterrows():
    folium.CircleMarker(
        location=[row["latitude"], row["longitude"]],
        radius=3,
        color="red",
        fill=True,
        fill_opacity=0.6
    ).add_to(marker_cluster)

la_map

Make this Notebook Trusted to load map: File -> Trust Notebook

Prepare Data

import pandas as pd

df = pd.read_csv("data/los_angeles.csv")

df["incident_date"] = pd.to_datetime(df["incident_date"], errors="coerce")
df["year"] = df["incident_date"].dt.year
df["month"] = df["incident_date"].dt.month
df["month_name"] = df["incident_date"].dt.strftime("%b")

df = df.dropna(subset=["year", "month"])

monthly_total = (
    df.groupby(["year", "month", "month_name"])
    .size()
    .reset_index(name="crime_count")
)

Crime Trends Over Time

import plotly.express as px

years = sorted(df["year"].unique())

fig = px.line(
    monthly_total,
    x="month_name",
    y="crime_count",
    color="year",
    markers=True,
    title="Monthly Crime Totals (Select Year)",
    template="plotly_white"
)

buttons = []

buttons.append(
    dict(
        label="All Years",
        method="update",
        args=[{"visible": [True] * len(years)}]
    )
)

for i, yr in enumerate(years):
    visibility = [False] * len(years)
    visibility[i] = True

    buttons.append(
        dict(
            label=str(yr),
            method="update",
            args=[{"visible": visibility}]
        )
    )

fig.update_layout(
    updatemenus=[
        dict(
            buttons=buttons,
            direction="down",
            x=1.15,
            y=1.0
        )
    ]
)

fig.update_xaxes(
    categoryorder="array",
    categoryarray=["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"]
)

fig

Top Crime Types Over Time (Top 5)

monthly_by_cat = (
    df.groupby(["year", "month", "month_name", "crime_description"])
    .size()
    .reset_index(name="crime_count")
)

top_categories = df["crime_description"].value_counts().head(5).index
monthly_by_cat = monthly_by_cat[monthly_by_cat["crime_description"].isin(top_categories)]

import plotly.express as px
import plotly.graph_objects as go

# --- Step 1: Keep only top 5 categories ---
top_categories = (
    df["crime_description"]
      .value_counts()
      .head(5)
      .index
)

filtered = monthly_by_cat[
    monthly_by_cat["crime_description"].isin(top_categories)
].copy()

# --- Step 2: available years ---
years = sorted(filtered["year"].unique())
default_year = years[0]

# --- Step 3: filter initial dataset ---
df_year = filtered[filtered["year"] == default_year]

# --- Step 4: build initial plot ---
fig = px.line(
    df_year,
    x="month_name",
    y="crime_count",
    color="crime_description",
    markers=True,
    title=f"Monthly Crime Trends by Category ({default_year})",
    template="plotly_white"
)

# --- Step 5: Year Dropdown ---
year_buttons = []

for yr in years:
    df_temp = filtered[filtered["year"] == yr]

    # Build x/y arrays for each category in correct order
    x_list = [
        df_temp[df_temp["crime_description"] == cat]["month_name"]
        for cat in top_categories
    ]
    y_list = [
        df_temp[df_temp["crime_description"] == cat]["crime_count"]
        for cat in top_categories
    ]

    year_buttons.append(
        dict(
            label=str(yr),
            method="update",
            args=[
                {
                    "x": x_list,
                    "y": y_list,
                },
                {
                    "title": f"Monthly Crime Trends by Category ({yr})"
                }
            ]
        )
    )

fig.update_layout(
    updatemenus=[
        dict(
            buttons=year_buttons,
            direction="down",
            x=1.15,
            y=1.05,
            showactive=True
        )
    ]
)

# --- Step 6: Month Ordering ---
fig.update_xaxes(
    categoryorder="array",
    categoryarray=["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"]
)

fig

Neighborhood Analysis

import plotly.express as px
import pandas as pd

df = pd.read_csv("data/los_angeles.csv")

top_neighborhoods = df["area_name"].value_counts().head(10)

fig = px.bar(
    x=top_neighborhoods.index,
    y=top_neighborhoods.values,
    labels={"x": "Neighborhood", "y": "Incidents"},
    title="Top 10 Neighborhoods by Number of Crimes – Los Angeles",
    text=top_neighborhoods.values
)

fig.update_traces(marker_color="tomato", textposition="outside")
fig.update_layout(
    xaxis_tickangle=35,
    template="simple_white",
    height=500
)

fig.show()

Crime Hotspots — Top ZIP Codes

df["zip_code"] = df["zip_code"].astype(str).str.replace(".0", "", regex=False)

zip_counts = (
    df.groupby("zip_code")
      .size()
      .reset_index(name="crime_count")
      .sort_values("crime_count", ascending=False)
)

top_zip = zip_counts.head(10)

import plotly.express as px

fig = px.bar(
    top_zip.sort_values("crime_count"),
    x="crime_count",
    y="zip_code",
    orientation="h",
    title="Top 10 ZIP Codes by Total Crime",
    labels={"zip_code": "ZIP Code", "crime_count": "Total Crimes"},
    template="plotly_white"
)

fig

Victim Age Distribution

import plotly.express as px

# 1. Clean age data
df_age_clean = df[(df["victim_age"] >= 0) & (df["victim_age"] <= 120)]

# 2. Select TOP 5 crime categories
top5_age = (
    df_age_clean["crime_description"]
      .value_counts()
      .head(5)
      .index
)

df_top5 = df_age_clean[df_age_clean["crime_description"].isin(top5_age)]

# 3. Build a clean histogram
fig = px.histogram(
    df_top5,
    x="victim_age",
    color="crime_description",
    nbins=40,
    barmode="overlay",
    opacity=0.6,
    title="Victim Age Distribution (Top 5 Crime Categories)",
    template="plotly_white"
)

fig.update_layout(
    legend_title_text="Crime Category",
    bargap=0.05
)

fig

Victim Age Groups

import pandas as pd
import plotly.express as px

# --- Clean age data ---
df_age_clean = df[(df["victim_age"] >= 0) & (df["victim_age"] <= 120)].copy()

df_age_clean.loc[:, "age_group"] = pd.cut(
    df_age_clean["victim_age"],
    bins=[0, 18, 30, 45, 60, 120],
    labels=["0–18", "19–30", "31–45", "46–60", "60+"]
)

# --- Step 1: Select Top 10 crimes by total count ---
top10_crimes = (
    df_age_clean["crime_description"]
      .value_counts()
      .head(10)
      .index
)

df_top10 = df_age_clean[df_age_clean["crime_description"].isin(top10_crimes)]

# --- Step 2: Group for plotting ---
age_group_df = (
    df_top10
        .dropna(subset=["age_group"])
        .groupby(["crime_description", "age_group"], observed=True)
        .size()
        .reset_index(name="count")
)

# --- Step 3: Plot ---
fig = px.bar(
    age_group_df,
    x="crime_description",
    y="count",
    color="age_group",
    barmode="stack",
    title="Victim Age Groups (Top 10 Crimes)",
    template="plotly_white"
)

# Make x labels readable
fig.update_layout(
    xaxis_tickangle=-45,
    legend_title_text="Age Group"
)

fig